Accuracy Improvement in Software Cost Estimation based on Selection of Relevant Features of Homogeneous Clusters

Software Cost Estimation (SCE) is one of the most widely used and effective activities in project management. In machine learning methods, some features have adverse effects on accuracy. Thus, preprocessing methods based on reducing non-effective features can improve accuracy in these methods. In clustering techniques, samples are categorized into different clusters according to their semantic similarity. Accordingly, in the proposed study, to improve SCE accuracy, first samples are clustered based on original features. Then, a feature selection (FS) technique is separately done for each cluster. The proposed FS method is based on a combination of filter and wrapper FS methods. The proposed method uses both filter and wrapper advantages in selecting effective features of each cluster, with less computational complexity and more accuracy. Furthermore, as the assessment criteria have significant impacts on wrapper methods, a fused criterion has also been used. The proposed method was applied to Desharnais, COCOMO81, COCONASA93, Kemerer, and Albrecht datasets, and the obtained Mean Magnitude of Relative Error (MMRE) for these datasets were 0.2173, 0.6489, 0.3129, 0.4898 and 0.4245, respectively. These results were compared with previous studies and showed improvement in the error rate of SCE.

Accuracy Improvement in Software Cost Estimation based on Selection of Relevant Features of Homogeneous Clusters Keywords:

Software Cost Estimation (SCE) , Software Effort Estimation (SEE) , Machine Learning methods , Clustering , Feature Selection